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Optical implementation of artificial neural networks has been attractinggreat attention due to its potential in parallel computation at speed of light.Although all-optical deep neural networks (AODNNs) with a few neurons have beenexperimentally demonstrated with acceptable errors recently, the feasibility oflarge scale AODNNs remains unknown because error might accumulate inevitablywith increasing number of neurons and connections. Here, we demonstrate ascalable AODNN with programmable linear operations and tunable nonlinearactivation functions. We verify its scalability by measuring and analyzingerrors propagating from a single neuron to the entire network. The feasibilityof AODNNs is further confirmed by recognizing handwritten digits and fashionsrespectively.

Scalability of all-optical neural networks based on spatial light modulators